Overview

Dataset statistics

Number of variables11
Number of observations2804
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory241.1 KiB
Average record size in memory88.0 B

Variable types

Categorical2
Numeric9

Warnings

df_index has a high cardinality: 2804 distinct values High cardinality
open is highly correlated with high and 3 other fieldsHigh correlation
high is highly correlated with open and 3 other fieldsHigh correlation
low is highly correlated with open and 3 other fieldsHigh correlation
adj_close is highly correlated with open and 3 other fieldsHigh correlation
volume is highly correlated with open and 3 other fieldsHigh correlation
RSI_14 is highly correlated with STO_14High correlation
STO_14 is highly correlated with RSI_14High correlation
open is highly correlated with high and 3 other fieldsHigh correlation
high is highly correlated with open and 3 other fieldsHigh correlation
low is highly correlated with open and 3 other fieldsHigh correlation
adj_close is highly correlated with open and 3 other fieldsHigh correlation
volume is highly correlated with open and 3 other fieldsHigh correlation
RSI_14 is highly correlated with STO_14High correlation
STO_14 is highly correlated with RSI_14High correlation
open is highly correlated with high and 2 other fieldsHigh correlation
high is highly correlated with open and 2 other fieldsHigh correlation
low is highly correlated with open and 2 other fieldsHigh correlation
adj_close is highly correlated with open and 2 other fieldsHigh correlation
RSI_14 is highly correlated with STO_14High correlation
STO_14 is highly correlated with RSI_14High correlation
RSI_14 is highly correlated with CHO and 2 other fieldsHigh correlation
CHO is highly correlated with RSI_14 and 5 other fieldsHigh correlation
volume is highly correlated with CHO and 5 other fieldsHigh correlation
STO_14 is highly correlated with RSI_14High correlation
high is highly correlated with CHO and 4 other fieldsHigh correlation
open is highly correlated with CHO and 4 other fieldsHigh correlation
adj_close is highly correlated with CHO and 4 other fieldsHigh correlation
return is highly correlated with RSI_14 and 1 other fieldsHigh correlation
low is highly correlated with CHO and 4 other fieldsHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
CHO has unique values Unique
STO_14 has 362 (12.9%) zeros Zeros

Reproduction

Analysis started2021-06-16 04:20:58.474991
Analysis finished2021-06-16 04:21:32.707220
Duration34.23 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct2804
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
2017-10-25
 
1
2020-10-23
 
1
2014-11-12
 
1
2014-03-05
 
1
2010-07-22
 
1
Other values (2799)
2799 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters28040
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2804 ?
Unique (%)100.0%

Sample

1st row2010-01-22
2nd row2010-01-26
3rd row2010-01-27
4th row2010-01-28
5th row2010-01-29

Common Values

ValueCountFrequency (%)
2017-10-251
 
< 0.1%
2020-10-231
 
< 0.1%
2014-11-121
 
< 0.1%
2014-03-051
 
< 0.1%
2010-07-221
 
< 0.1%
2018-06-281
 
< 0.1%
2017-01-201
 
< 0.1%
2019-03-141
 
< 0.1%
2012-04-201
 
< 0.1%
2014-03-171
 
< 0.1%
Other values (2794)2794
99.6%

Length

2021-06-16T01:21:33.480043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-10-251
 
< 0.1%
2020-10-231
 
< 0.1%
2014-11-121
 
< 0.1%
2014-03-051
 
< 0.1%
2010-07-221
 
< 0.1%
2018-06-281
 
< 0.1%
2017-01-201
 
< 0.1%
2019-03-141
 
< 0.1%
2012-04-201
 
< 0.1%
2014-03-171
 
< 0.1%
Other values (2794)2794
99.6%

Most occurring characters

ValueCountFrequency (%)
06750
24.1%
-5608
20.0%
15161
18.4%
25025
17.9%
3922
 
3.3%
8777
 
2.8%
4766
 
2.7%
6762
 
2.7%
5762
 
2.7%
7760
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22432
80.0%
Dash Punctuation5608
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06750
30.1%
15161
23.0%
25025
22.4%
3922
 
4.1%
8777
 
3.5%
4766
 
3.4%
6762
 
3.4%
5762
 
3.4%
7760
 
3.4%
9747
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
-5608
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common28040
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06750
24.1%
-5608
20.0%
15161
18.4%
25025
17.9%
3922
 
3.3%
8777
 
2.8%
4766
 
2.7%
6762
 
2.7%
5762
 
2.7%
7760
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII28040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06750
24.1%
-5608
20.0%
15161
18.4%
25025
17.9%
3922
 
3.3%
8777
 
2.8%
4766
 
2.7%
6762
 
2.7%
5762
 
2.7%
7760
 
2.7%

open
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1988
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.79322753
Minimum36.32
Maximum119.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2021-06-16T01:21:33.666750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum36.32
5-th percentile45.953
Q153.1
median62.055
Q377.64
95-th percentile109.41
Maximum119.89
Range83.57
Interquartile range (IQR)24.54

Descriptive statistics

Standard deviation19.36664502
Coefficient of variation (CV)0.2856722673
Kurtosis-0.1051485656
Mean67.79322753
Median Absolute Deviation (MAD)10.2
Skewness0.9348437557
Sum190092.21
Variance375.0669393
MonotonicityNot monotonic
2021-06-16T01:21:33.868534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5613
 
0.5%
559
 
0.3%
539
 
0.3%
508
 
0.3%
588
 
0.3%
698
 
0.3%
57.57
 
0.2%
52.57
 
0.2%
687
 
0.2%
70.47
 
0.2%
Other values (1978)2721
97.0%
ValueCountFrequency (%)
36.321
 
< 0.1%
36.491
 
< 0.1%
36.61
 
< 0.1%
36.751
 
< 0.1%
37.191
 
< 0.1%
37.481
 
< 0.1%
37.513
0.1%
37.731
 
< 0.1%
37.851
 
< 0.1%
38.011
 
< 0.1%
ValueCountFrequency (%)
119.891
< 0.1%
119.751
< 0.1%
119.461
< 0.1%
119.441
< 0.1%
119.051
< 0.1%
118.941
< 0.1%
118.791
< 0.1%
118.491
< 0.1%
118.271
< 0.1%
118.261
< 0.1%

high
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2044
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.41776034
Minimum36.61
Maximum120.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2021-06-16T01:21:34.078548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum36.61
5-th percentile46.5
Q153.7275
median62.775
Q378.67
95-th percentile110.601
Maximum120.89
Range84.28
Interquartile range (IQR)24.9425

Descriptive statistics

Standard deviation19.4604303
Coefficient of variation (CV)0.2844353601
Kurtosis-0.1017436866
Mean68.41776034
Median Absolute Deviation (MAD)10.355
Skewness0.9387675829
Sum191843.4
Variance378.7083475
MonotonicityNot monotonic
2021-06-16T01:21:34.283024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5613
 
0.5%
659
 
0.3%
609
 
0.3%
639
 
0.3%
558
 
0.3%
597
 
0.2%
63.997
 
0.2%
52.997
 
0.2%
49.357
 
0.2%
50.16
 
0.2%
Other values (2034)2722
97.1%
ValueCountFrequency (%)
36.611
< 0.1%
36.751
< 0.1%
36.961
< 0.1%
37.31
< 0.1%
37.511
< 0.1%
37.631
< 0.1%
37.681
< 0.1%
37.851
< 0.1%
38.011
< 0.1%
38.331
< 0.1%
ValueCountFrequency (%)
120.891
< 0.1%
120.661
< 0.1%
120.151
< 0.1%
119.971
< 0.1%
119.931
< 0.1%
119.831
< 0.1%
119.581
< 0.1%
119.51
< 0.1%
119.441
< 0.1%
119.291
< 0.1%

low
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2097
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.09756063
Minimum36.01
Maximum119.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2021-06-16T01:21:34.490936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum36.01
5-th percentile45.123
Q152.4975
median61.56
Q376.97
95-th percentile108.639
Maximum119.53
Range83.52
Interquartile range (IQR)24.4725

Descriptive statistics

Standard deviation19.25837955
Coefficient of variation (CV)0.2870205619
Kurtosis-0.09038330103
Mean67.09756063
Median Absolute Deviation (MAD)10.31
Skewness0.936066514
Sum188141.56
Variance370.8851831
MonotonicityNot monotonic
2021-06-16T01:21:34.691373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
559
 
0.3%
56.57
 
0.2%
51.76
 
0.2%
55.56
 
0.2%
566
 
0.2%
546
 
0.2%
596
 
0.2%
57.56
 
0.2%
69.55
 
0.2%
54.55
 
0.2%
Other values (2087)2742
97.8%
ValueCountFrequency (%)
36.011
< 0.1%
36.051
< 0.1%
36.21
< 0.1%
36.241
< 0.1%
36.631
< 0.1%
36.691
< 0.1%
36.751
< 0.1%
36.821
< 0.1%
36.831
< 0.1%
37.361
< 0.1%
ValueCountFrequency (%)
119.531
< 0.1%
118.792
0.1%
118.571
< 0.1%
118.051
< 0.1%
117.961
< 0.1%
117.841
< 0.1%
117.811
< 0.1%
117.741
< 0.1%
117.721
< 0.1%
117.481
< 0.1%

adj_close
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2028
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.68993937
Minimum36.45
Maximum120.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2021-06-16T01:21:34.903212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum36.45
5-th percentile45.853
Q152.9775
median62
Q377.725
95-th percentile109.4485
Maximum120.72
Range84.27
Interquartile range (IQR)24.7475

Descriptive statistics

Standard deviation19.40675783
Coefficient of variation (CV)0.2867007714
Kurtosis-0.09932680875
Mean67.68993937
Median Absolute Deviation (MAD)10.3
Skewness0.9396392631
Sum189802.59
Variance376.6222496
MonotonicityNot monotonic
2021-06-16T01:21:35.455239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5510
 
0.4%
548
 
0.3%
568
 
0.3%
64.017
 
0.2%
706
 
0.2%
53.56
 
0.2%
51.86
 
0.2%
70.255
 
0.2%
55.055
 
0.2%
51.525
 
0.2%
Other values (2018)2738
97.6%
ValueCountFrequency (%)
36.451
< 0.1%
36.51
< 0.1%
36.61
< 0.1%
36.751
< 0.1%
36.851
< 0.1%
36.861
< 0.1%
37.41
< 0.1%
37.482
0.1%
37.521
< 0.1%
37.751
< 0.1%
ValueCountFrequency (%)
120.721
< 0.1%
120.41
< 0.1%
119.591
< 0.1%
119.451
< 0.1%
119.261
< 0.1%
119.241
< 0.1%
118.681
< 0.1%
118.621
< 0.1%
118.41
< 0.1%
118.371
< 0.1%

volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2260
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2898018.166
Minimum0
Maximum45899510
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2021-06-16T01:21:35.656287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q1216838
median1830010
Q33961882.5
95-th percentile10100073.3
Maximum45899510
Range45899510
Interquartile range (IQR)3745044.5

Descriptive statistics

Standard deviation3865365.685
Coefficient of variation (CV)1.333796223
Kurtosis17.16526454
Mean2898018.166
Median Absolute Deviation (MAD)1829894.5
Skewness3.152217722
Sum8126042938
Variance1.494105188 × 1013
MonotonicityNot monotonic
2021-06-16T01:21:35.875126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3422
 
0.8%
2619
 
0.7%
2417
 
0.6%
2517
 
0.6%
1217
 
0.6%
2817
 
0.6%
1516
 
0.6%
2216
 
0.6%
2115
 
0.5%
2915
 
0.5%
Other values (2250)2633
93.9%
ValueCountFrequency (%)
02
 
0.1%
14
0.1%
22
 
0.1%
34
0.1%
52
 
0.1%
67
0.2%
78
0.3%
85
0.2%
95
0.2%
107
0.2%
ValueCountFrequency (%)
458995101
< 0.1%
371606601
< 0.1%
342899401
< 0.1%
312377001
< 0.1%
300978301
< 0.1%
289125301
< 0.1%
287283601
< 0.1%
283982501
< 0.1%
275268701
< 0.1%
268255671
< 0.1%

RSI_14
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2787
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.79138584
Minimum14.38390501
Maximum82.2701558
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2021-06-16T01:21:36.090081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum14.38390501
5-th percentile34.07193908
Q144.37680073
median51.40153149
Q359.39856181
95-th percentile69.91217465
Maximum82.2701558
Range67.8862508
Interquartile range (IQR)15.02176107

Descriptive statistics

Standard deviation10.91424478
Coefficient of variation (CV)0.2107347507
Kurtosis-0.13726454
Mean51.79138584
Median Absolute Deviation (MAD)7.42926744
Skewness-0.003104048169
Sum145223.0459
Variance119.1207391
MonotonicityNot monotonic
2021-06-16T01:21:36.301299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.319032072
 
0.1%
42.914567362
 
0.1%
58.390719592
 
0.1%
39.885476462
 
0.1%
60.006181122
 
0.1%
73.863296552
 
0.1%
47.588232532
 
0.1%
37.834200672
 
0.1%
32.992169822
 
0.1%
46.757493722
 
0.1%
Other values (2777)2784
99.3%
ValueCountFrequency (%)
14.383905011
< 0.1%
19.193220281
< 0.1%
19.232505121
< 0.1%
20.246013791
< 0.1%
20.435199011
< 0.1%
20.886366221
< 0.1%
21.040645351
< 0.1%
21.045530961
< 0.1%
21.214976321
< 0.1%
22.139585741
< 0.1%
ValueCountFrequency (%)
82.27015581
< 0.1%
82.112755831
< 0.1%
81.668178661
< 0.1%
81.637831521
< 0.1%
81.438446681
< 0.1%
81.301891571
< 0.1%
80.944106191
< 0.1%
80.460900851
< 0.1%
79.610272231
< 0.1%
79.438506831
< 0.1%

STO_14
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1941
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.86221418
Minimum0
Maximum100
Zeros362
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2021-06-16T01:21:36.523183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120.16578184
median59.22604219
Q391.46362728
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)71.29784545

Descriptive statistics

Standard deviation36.46953461
Coefficient of variation (CV)0.6647477713
Kurtosis-1.415159177
Mean54.86221418
Median Absolute Deviation (MAD)34.79673529
Skewness-0.2058783452
Sum153833.6486
Variance1330.026954
MonotonicityNot monotonic
2021-06-16T01:21:36.729868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100476
 
17.0%
0362
 
12.9%
4.7826086963
 
0.1%
46.666666672
 
0.1%
44.488188982
 
0.1%
50.669642862
 
0.1%
9.1240875912
 
0.1%
702
 
0.1%
16.759776542
 
0.1%
43.172690762
 
0.1%
Other values (1931)1949
69.5%
ValueCountFrequency (%)
0362
12.9%
0.18867924531
 
< 0.1%
0.20661157021
 
< 0.1%
0.22222222221
 
< 0.1%
0.25641025641
 
< 0.1%
0.32051282051
 
< 0.1%
0.44150110381
 
< 0.1%
0.45146726861
 
< 0.1%
0.55096418731
 
< 0.1%
0.55401662051
 
< 0.1%
ValueCountFrequency (%)
100476
17.0%
99.789029541
 
< 0.1%
99.751243781
 
< 0.1%
99.73821991
 
< 0.1%
99.695121951
 
< 0.1%
99.685534591
 
< 0.1%
99.58620691
 
< 0.1%
99.516908211
 
< 0.1%
99.498746871
 
< 0.1%
99.460916441
 
< 0.1%

CHO
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct2804
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49673.18421
Minimum-11779644.18
Maximum11872803.46
Zeros0
Zeros (%)0.0%
Negative1524
Negative (%)54.4%
Memory size22.0 KiB
2021-06-16T01:21:36.938737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-11779644.18
5-th percentile-3704335.069
Q1-544780.5208
median-40.61082477
Q3668540.8088
95-th percentile3722691.593
Maximum11872803.46
Range23652447.64
Interquartile range (IQR)1213321.33

Descriptive statistics

Standard deviation2280116.031
Coefficient of variation (CV)45.9023529
Kurtosis5.006596298
Mean49673.18421
Median Absolute Deviation (MAD)609239.8522
Skewness-0.1008284634
Sum139283608.5
Variance5.198929115 × 1012
MonotonicityNot monotonic
2021-06-16T01:21:37.144078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
747584.05681
 
< 0.1%
-28.291425691
 
< 0.1%
-63104.342731
 
< 0.1%
30.114423881
 
< 0.1%
-53.333276331
 
< 0.1%
1465195.8061
 
< 0.1%
-1424209.8891
 
< 0.1%
-11.837895771
 
< 0.1%
-3357827.8181
 
< 0.1%
2325051.641
 
< 0.1%
Other values (2794)2794
99.6%
ValueCountFrequency (%)
-11779644.181
< 0.1%
-11755122.581
< 0.1%
-10641752.021
< 0.1%
-10635639.381
< 0.1%
-10441979.781
< 0.1%
-9892370.2291
< 0.1%
-9850856.8311
< 0.1%
-9666719.3981
< 0.1%
-9389014.8731
< 0.1%
-9318371.0571
< 0.1%
ValueCountFrequency (%)
11872803.461
< 0.1%
11441018.891
< 0.1%
10906629.971
< 0.1%
10892257.921
< 0.1%
10743244.471
< 0.1%
10475236.131
< 0.1%
9734150.041
< 0.1%
9710380.0951
< 0.1%
9457318.1551
< 0.1%
9378587.1341
< 0.1%

return
Real number (ℝ)

HIGH CORRELATION

Distinct2767
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0004091337009
Minimum-0.1457470144
Maximum0.1339514979
Zeros25
Zeros (%)0.9%
Negative1351
Negative (%)48.2%
Memory size22.0 KiB
2021-06-16T01:21:37.344460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1457470144
5-th percentile-0.02762426288
Q1-0.009295309773
median0.000326768495
Q30.01029545987
95-th percentile0.02950649635
Maximum0.1339514979
Range0.2796985122
Interquartile range (IQR)0.01959076964

Descriptive statistics

Standard deviation0.01962155952
Coefficient of variation (CV)47.95879555
Kurtosis6.860064297
Mean0.0004091337009
Median Absolute Deviation (MAD)0.009767486183
Skewness-0.2057492295
Sum1.147210897
Variance0.0003850055979
MonotonicityNot monotonic
2021-06-16T01:21:37.540056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
025
 
0.9%
-0.018518518523
 
0.1%
-0.018181818182
 
0.1%
0.0022
 
0.1%
-0.000181785132
 
0.1%
0.024590163932
 
0.1%
0.0046189376442
 
0.1%
0.0031152647982
 
0.1%
-0.004950495052
 
0.1%
0.015531660692
 
0.1%
Other values (2757)2760
98.4%
ValueCountFrequency (%)
-0.14574701441
< 0.1%
-0.13951440431
< 0.1%
-0.1239406781
< 0.1%
-0.11714719271
< 0.1%
-0.097032586931
< 0.1%
-0.094778481011
< 0.1%
-0.089272727271
< 0.1%
-0.086637073321
< 0.1%
-0.086421921661
< 0.1%
-0.086282762581
< 0.1%
ValueCountFrequency (%)
0.13395149791
< 0.1%
0.11550360951
< 0.1%
0.11214087121
< 0.1%
0.10238796211
< 0.1%
0.10120608291
< 0.1%
0.093105272441
< 0.1%
0.086956521741
< 0.1%
0.082258064521
< 0.1%
0.071704957681
< 0.1%
0.070716395861
< 0.1%

Target
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
1.0
1429 
0.0
1375 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8412
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01429
51.0%
0.01375
49.0%

Length

2021-06-16T01:21:37.870800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-16T01:21:37.976253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01429
51.0%
0.01375
49.0%

Most occurring characters

ValueCountFrequency (%)
04179
49.7%
.2804
33.3%
11429
 
17.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5608
66.7%
Other Punctuation2804
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04179
74.5%
11429
 
25.5%
Other Punctuation
ValueCountFrequency (%)
.2804
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04179
49.7%
.2804
33.3%
11429
 
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04179
49.7%
.2804
33.3%
11429
 
17.0%

Interactions

2021-06-16T01:21:17.401003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:18.031345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:18.193758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:18.353898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:18.519576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:18.683070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:18.856309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:19.020210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:19.209999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:19.376308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:19.535765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:19.696859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:19.857320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:20.018070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:20.186405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:20.355222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:20.519854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:20.681608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:20.849910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:21.011838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:21.169866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:21.329859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:21.494794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:21.655924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:21.828786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:21.992020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:22.157223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:22.323233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:22.492054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:22.656522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:22.814711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:22.978106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:23.139019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:23.306060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:23.473658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:23.641453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:23.807914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:23.976250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:24.143078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:24.609848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:24.772712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:24.941086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:25.117140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:25.288667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:25.454099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:25.626740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:25.798612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:25.971434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:26.145621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:26.320190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:26.494948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:26.683643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:26.864733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:27.039655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:27.223268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:27.387892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:27.552146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:27.716724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:27.887652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:28.054402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:28.230191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:28.404388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:28.570588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:28.743000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:28.908024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:29.071787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:29.234482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:29.408233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:29.575406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:29.746007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:29.918708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:30.084448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:30.253037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:30.426506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:30.592988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:30.759398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:30.934178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:31.102547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:31.281227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:31.457722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-16T01:21:31.633497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-06-16T01:21:38.083847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-16T01:21:38.348806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-16T01:21:38.610764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-16T01:21:38.861560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-16T01:21:31.980107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-16T01:21:32.542125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexopenhighlowadj_closevolumeRSI_14STO_14CHOreturnTarget
02010-01-2265.6065.9664.9165.5965371126.5508680.000000-230526.994114-0.0047040.0
12010-01-2665.0565.3664.1264.9845709524.5499470.000000-153660.903303-0.0093000.0
22010-01-2764.9965.0063.9064.9044401724.2913950.0000007344.691172-0.0012311.0
32010-01-2865.0666.4963.9865.2024396127.3800535.88235370378.1055060.0046220.0
42010-01-2965.5065.9964.7064.9012933126.2277410.00000061375.649488-0.0046011.0
52010-02-0165.4066.1664.7966.0517229037.14764422.54902098129.6288410.0177201.0
62010-02-0266.9566.9565.9966.7022122842.34259035.294118137973.3624700.0098410.0
72010-02-0366.5066.6566.2066.5926983941.71423136.739130204692.472063-0.0016490.0
82010-02-0466.1066.2163.1263.4028605828.5038260.000000138855.046062-0.0479050.0
92010-02-0562.3163.4960.7662.0068070724.7929070.00000079464.247554-0.0220821.0

Last rows

df_indexopenhighlowadj_closevolumeRSI_14STO_14CHOreturnTarget
27942021-05-17117.07118.41117.06118.31527918260.340676100.0000002.077768e+060.0091270.0
27952021-05-18118.09118.88117.74118.19545677359.83597997.6190482.173268e+06-0.0010140.0
27962021-05-19117.07118.54116.94118.01484373259.03829794.0476192.534916e+06-0.0015230.0
27972021-05-20118.05118.14117.48117.91483320258.57113992.0634922.918428e+06-0.0008471.0
27982021-05-21118.01118.13117.10118.01516906858.92119194.0476194.071479e+060.0008481.0
27992021-05-24118.26119.50117.96119.26578864463.116431100.0000005.440808e+060.0105920.0
28002021-05-25119.89119.97118.05118.40569765458.67649978.4461154.354429e+06-0.0072111.0
28012021-05-26118.94119.58118.79119.24471689461.52354599.4987473.723120e+060.0070951.0
28022021-05-27119.46119.83118.79119.59603874662.677378100.0000004.161002e+060.0029351.0
28032021-05-28119.75120.89119.53120.72599142366.201422100.0000005.391634e+060.0094491.0